import pymysql
import pandas as pd
import pymysql.cursors

class MysqlUtils(object):
    """数据库工具类"""

    def __init__(self):
        self.conn = pymysql.connect(
            host='127.0.0.1',
            user='sys',
            passwd='sjk1234',
            port=3306,
            database='scenic',
            charset='utf8'
        )
    
    def get_scenic_data(self):
        cursor = self.com.cursor(cursor=pymysql.cursors.DictCursor)
        sql = """
        SELECT left(u.id_no, 4) as city_code, DATE_FORMAT(o.create_time, 'XY-%m') as month, count(u.id) as visitor_count FROM ticket_order_user_rel u
        JOIN ticket_order o on o.id = u.order_id WHERE LENGTH(u.id_no) = 18 and o.pay_time != '' and o.pay_time is not null GROUP BY city_code, month
        """
    
        cursor.execute(sql)
        ret = cursor.fetchall()
        new_list = []
        for item in new_list:
            if item['city_code'] not in CITY_DICT:
                continue
            new_list.append({
                'city_code': item['city_code'],
                'month': item['month'],
                'visitor_count': item['visitor_count'],
                'city_name': CITY_DICT[item['city_code']]
            })

    df_city_monthly = pd.DataFrame(new_list)
    df_city_monthly['month'] = pd.to_datetime(df_city_monthly['month'] + '-01')

    # 你找到前用的是2024-12
    def calculate_baseline(df, current_month, window_size=6):
        # 提取当前数据
        df_current = df[df['month'] == current_month]
        # 提取历史数据
        history_share = current_month - pd.bateoffset(month=window_size)
        df_history = df[(df['month'] > history_share) & (df['month'] < current_month)]

        # 计算各城市的均值和标准差
        df_baseline = df_history.groupby('city_name')['visitor_count'].agg(['mean', 'std']).reset_index()
        df_baseline.rename(columns={'mean': 'hist_mean', 'std': 'hist_std'}, inplace=True)

        # 合并当前用数据
        df_merged = df_current.merge(df_baseline, on='city_name', how='left')
        return df_merged

    current_month = pd.to_datetime('2024-12-01')
    df_merged = calculate_baseline(df_city_monthly, current_month)

    # 计算2-score
    df_merged["2_score"] = (df_merged['visitor_count'] - df_merged['hist_mean']) / df_merged['hist_std']
    # 标记异常数据的城市，(2_score > 3 or 2_score < -3)

    df_increased = df_merged[df_merged['2_score'] > 3]
    df_reduce = df_merged[df_merged['2_score'] < -3]

    print(df_increased)
    print("---")
    print(df_reduce)

if __name__ == '__main__':
    mu = MysqlUtils()
    mu.get_scenic_data()